
# set-up ====
enumerate <- 0L
errorfun <- function(tt) {
  
  if(isFALSE(tt)) stop(print(tt))
}

test_make_dims <- function(n) {
  
  # make dimensions that are randomly of size 1 or 5:
  out <- lapply(1:n, \(n)sample(c(1, 5), 1)) |> unlist()
  
  # check if the dimensions produce a too large object.
  # If so, replace one >1L dimension with 1L
  if(prod(out) > 5000L) {
    ind <- which(out > 1L)[1L]
    out[ind] <- 1L
  }
  return(out)
}
.return_missing <- broadcast:::.return_missing

prec <- sqrt(.Machine$double.eps)


# Note:
# These are primarily dimensional consistency tests.
# For tests about whether the rel ops are actually accurate,
# see test-relop_precision.R


test_numeric_x <- function(op, basefun) {
  
  i <- 1L
  x.data <- sample(c(sample(-10.5:10.5), NA, NaN, Inf, -Inf), 100, TRUE)
  
  for(iSample in 1:10) { # re-do tests with different random configurations
    y.data <- list(
      sample(c(TRUE, FALSE, NA), 100, TRUE), # logical
      sample(c(-10:10, NA), 100, TRUE), # integer
      sample(-10.5:10.5, 100, TRUE) # double
    )
    for(iDimX in sample(1:8, 3L)) { # different dimensions for x
      x.dim <- test_make_dims(iDimX)
      x.len <- prod(x.dim)
      for(iDimY in sample(1:8, 3L)) { # different dimensions for y
        y.dim <- test_make_dims(iDimY)
        y.len <- prod(y.dim)
        
        x <- array(x.data, dim = x.dim)
        for(iDataY in 1:length(y.data)) { # different data types for y
          y <- array(y.data[[iDataY]][1:y.len], dim = y.dim)
          
          # PREPARE FOR TEST
          tdim <- bc_dim(x, y)
          # print(x)
          # print(y)
          # print(tdim)
          # cat("\n")
          
          
          # DO TESTS BY CASE:
          if(is.null(tdim)) {
            # CASE 1: result has no dimensions (for ex. when x and y are both scalars)
            expected[[i]] <- basefun(as_dbl(drop(x)), as_dbl(drop(y)))
            attributes(expected[[i]]) <- NULL # must be a vector if tdim == NULL
            out[[i]] <- bc.d(x, y, op)
          }
          else if(length(y) == 1L && length(x) == 1L) {
            # CASE 2: x and y are both scalar arrays
            expected[[i]] <- basefun(as.double(x), as.double(y))
            out[[i]] <- bc.d(x, y, op)
          }
          else if(length(x) == 1L && length(y) > 1L) {
            # CASE 3: x is scalar, y is not
            expected[[i]] <- basefun(as.double(x), rep_dim(as_dbl(y), tdim))
            out[[i]] <- bc.d(x, y, op)
          }
          else if(length(y) == 1L && length(x) > 1L) {
            # CASE 4: y is scalar, x is not
            expected[[i]] <- basefun(rep_dim(as_dbl(x), tdim), as.double(y))
            out[[i]] <- bc.d(x, y, op)
          }
          else {
            # CASE 5: x and y are both non-reducible arrays
            expected[[i]] <- basefun(rep_dim(as_dbl(x), tdim), rep_dim(as_dbl(y), tdim))
            out[[i]] <- bc.d(x, y, op)
          }
          # END CASES
          
          # R is sometimes inconsistent whether it returns NA or NaN
          # for example: NaN + NaN = NA, but NaN - NaN = NaN
          # the 'broadcast' package prefers to remain consistent in all NA/NaN cases
          # the following code is meant to ensure NaN results turn to NA, like 'broadcast' does
          ind.NaN <- is.nan(expected[[i]])
          expected[[i]][ind.NaN] <- .return_missing(expected[[i]][ind.NaN])
          ind.NaN <- is.nan(out[[i]])
          out[[i]][ind.NaN] <- .return_missing(out[[i]][ind.NaN])
          
          # ensure correct dimensions:
          dim(expected[[i]]) <- tdim
          
          i <- i + 1L
        }
      }
    }
  }
  return(list(expected = expected, out = out, i = i))
}


test_numeric_y <- function(op, basefun) {
  
  i <- 1L
  y.data <- sample(c(sample(-10.5:10.5), NA, NaN, Inf, -Inf), 100, TRUE)
  
  for(iSample in 1:10) { # re-do tests with different random configurations
    x.data <- list(
      sample(c(TRUE, FALSE, NA), 100, TRUE), # logical
      sample(c(-10:10, NA), 100, TRUE), # integer
      sample(-10.5:10.5, 100, TRUE) # double
    )
    for(iDimX in sample(1:8, 3L)) { # different dimensions for x
      x.dim <- test_make_dims(iDimX)
      x.len <- prod(x.dim)
      for(iDimY in sample(1:8, 3L)) { # different dimensions for y
        y.dim <- test_make_dims(iDimY)
        y.len <- prod(y.dim)
        
        y <- array(y.data, dim = y.dim)
        for(iDataX in 1:length(x.data)) { # different data types for x
          x <- array(x.data[[iDataX]][1:x.len], dim = x.dim)
          
          # PREPARE FOR TEST
          tdim <- bc_dim(x, y)
          # print(x)
          # print(y)
          # print(tdim)
          # cat("\n")
          
          
          # DO TESTS BY CASE:
          if(is.null(tdim)) {
            # CASE 1: result has no dimensions (for ex. when x and y are both scalars)
            expected[[i]] <- basefun(as_dbl(drop(x)), as_dbl(drop(y)))
            attributes(expected[[i]]) <- NULL # must be a vector if tdim == NULL
            out[[i]] <- bc.d(x, y, op)
          }
          else if(length(y) == 1L && length(x) == 1L) {
            # CASE 2: x and y are both scalar arrays
            expected[[i]] <- basefun(as.double(x), as.double(y))
            out[[i]] <- bc.d(x, y, op)
          }
          else if(length(x) == 1L && length(y) > 1L) {
            # CASE 3: x is scalar, y is not
            expected[[i]] <- basefun(as.double(x), rep_dim(as_dbl(y), tdim))
            out[[i]] <- bc.d(x, y, op)
          }
          else if(length(y) == 1L && length(x) > 1L) {
            # CASE 4: y is scalar, x is not
            expected[[i]] <- basefun(rep_dim(as_dbl(x), tdim), as.double(y))
            out[[i]] <- bc.d(x, y, op)
          }
          else {
            # CASE 5: x and y are both non-reducible arrays
            expected[[i]] <- basefun(rep_dim(as_dbl(x), tdim), rep_dim(as_dbl(y), tdim))
            out[[i]] <- bc.d(x, y, op)
          }
          # END CASES
          
          # R is sometimes inconsistent whether it returns NA or NaN
          # for example: NaN + NaN = NA, but NaN - NaN = NaN
          # the 'broadcast' package prefers to remain consistent in all NA/NaN cases
          # the following code is meant to ensure NaN results turn to NA, like 'broadcast' does
          ind.NaN <- is.nan(expected[[i]])
          expected[[i]][ind.NaN] <- .return_missing(expected[[i]][ind.NaN])
          ind.NaN <- is.nan(out[[i]])
          out[[i]][ind.NaN] <- .return_missing(out[[i]][ind.NaN])
          
          # ensure correct dimensions:
          dim(expected[[i]]) <- tdim
          
          i <- i + 1L
        }
      }
    }
  }
  return(list(expected = expected, out = out, i = i))
}


# equals, numeric x ====
nres <- 10 * 5 * 5 * 3 # number of tests performed here
expected <- out <- vector("list", nres)
op <- "d=="

i <- 1L
x.data <- sample(c(sample(-10.5:10.5), NA, NaN, Inf, -Inf), 100, TRUE)
basefun <- function(x, y) {
  out <- abs(x - y) < prec
  return(out)
}
tests <- test_numeric_x(op, basefun)
enumerate <- enumerate + tests$i # count number of tests
# test results:
expect_equal(
  tests$expected, tests$out
)



# equals, numeric y ====
nres <- 10 * 5 * 5 * 3 # number of tests performed here
expected <- out <- vector("list", nres)
op <- "d=="

i <- 1L
y.data <- sample(c(sample(-10.5:10.5), NA, NaN, Inf, -Inf), 100, TRUE)
basefun <- function(x, y) {
  out <- abs(x - y) < prec
  return(out)
}
tests <- test_numeric_y(op, basefun)

enumerate <- enumerate + tests$i # count number of tests
# test results:
expect_equal(
  tests$expected, tests$out
)


# not-equals, numeric x ====
nres <- 10 * 5 * 5 * 3 # number of tests performed here
expected <- out <- vector("list", nres)
op <- "d!="

i <- 1L
x.data <- sample(c(sample(-10.5:10.5), NA, NaN, Inf, -Inf), 100, TRUE)
basefun <- function(x, y) {
  out <- abs(x - y) >= prec
  return(out)
}
tests <- test_numeric_x(op, basefun)
enumerate <- enumerate + tests$i # count number of tests
# test results:
expect_equal(
  tests$expected, tests$out
)



# not-equals, numeric y ====
nres <- 10 * 5 * 5 * 3 # number of tests performed here
expected <- out <- vector("list", nres)
op <- "d!="

i <- 1L
y.data <- sample(c(sample(-10.5:10.5), NA, NaN, Inf, -Inf), 100, TRUE)
basefun <- function(x, y) {
  out <- abs(x - y) >= prec
  return(out)
}
tests <- test_numeric_y(op, basefun)
enumerate <- enumerate + tests$i # count number of tests
# test results:
expect_equal(
  tests$expected, tests$out
)



# smaller than, numeric x ====
nres <- 10 * 5 * 5 * 3 # number of tests performed here
expected <- out <- vector("list", nres)
op <- "d<"

i <- 1L
x.data <- sample(c(sample(-10.5:10.5), NA, NaN, Inf, -Inf), 100, TRUE)
basefun <- function(x, y) {
  out <- (x - y) <= -prec
  return(out)
}
tests <- test_numeric_x(op, basefun)
enumerate <- enumerate + tests$i # count number of tests
# test results:
expect_equal(
  tests$expected, tests$out
)


# smaller than, numeric y ====
nres <- 10 * 5 * 5 * 3 # number of tests performed here
expected <- out <- vector("list", nres)
op <- "d<"

i <- 1L
y.data <- sample(c(sample(-10.5:10.5), NA, NaN, Inf, -Inf), 100, TRUE)
basefun <- function(x, y) {
  out <- (x - y) <= -prec
  return(out)
}
tests <- test_numeric_y(op, basefun)
enumerate <- enumerate + tests$i # count number of tests
# test results:
expect_equal(
  tests$expected, tests$out
)





# greater than, numeric x ====
nres <- 10 * 5 * 5 * 3 # number of tests performed here
expected <- out <- vector("list", nres)
op <- "d>"

i <- 1L
x.data <- sample(c(sample(-10.5:10.5), NA, NaN, Inf, -Inf), 100, TRUE)
basefun <- function(x, y) {
  out <- (x - y) >= prec
  return(out)
}
tests <- test_numeric_x(op, basefun)
enumerate <- enumerate + tests$i # count number of tests
# test results:
expect_equal(
  tests$expected, tests$out
)


# greater than, numeric y ====
nres <- 10 * 5 * 5 * 3 # number of tests performed here
expected <- out <- vector("list", nres)
op <- "d>"

i <- 1L
y.data <- sample(c(sample(-10.5:10.5), NA, NaN, Inf, -Inf), 100, TRUE)
basefun <- function(x, y) {
  out <- (x - y) >= prec
  return(out)
}
tests <- test_numeric_y(op, basefun)
enumerate <- enumerate + tests$i # count number of tests
# test results:
expect_equal(
  tests$expected, tests$out
)




# se, numeric x ====
nres <- 10 * 5 * 5 * 3 # number of tests performed here
expected <- out <- vector("list", nres)
op <- "d<="

i <- 1L
x.data <- sample(c(sample(-10.5:10.5), NA, NaN, Inf, -Inf), 100, TRUE)
basefun <- function(x, y) {
  out <- (x - y) < prec
  return(out)
}
tests <- test_numeric_x(op, basefun)
enumerate <- enumerate + tests$i # count number of tests
# test results:
expect_equal(
  tests$expected, tests$out
)



# se, numeric y ====
nres <- 10 * 5 * 5 * 3 # number of tests performed here
expected <- out <- vector("list", nres)
op <- "d<="

i <- 1L
y.data <- sample(c(sample(-10.5:10.5), NA, NaN, Inf, -Inf), 100, TRUE)
basefun <- function(x, y) {
  out <- (x - y) < prec
  return(out)
}
tests <- test_numeric_y(op, basefun)
enumerate <- enumerate + tests$i # count number of tests
# test results:
expect_equal(
  tests$expected, tests$out
)





# ge, numeric x ====
nres <- 10 * 5 * 5 * 3 # number of tests performed here
expected <- out <- vector("list", nres)
op <- "d>="

i <- 1L
x.data <- sample(c(sample(-10.5:10.5), NA, NaN, Inf, -Inf), 100, TRUE)
basefun <- function(x, y) {
  out <- (x - y) > -prec
  return(out)
}
tests <- test_numeric_x(op, basefun)
enumerate <- enumerate + tests$i # count number of tests
# test results:
expect_equal(
  tests$expected, tests$out
)



# ge, numeric y ====
nres <- 10 * 5 * 5 * 3 # number of tests performed here
expected <- out <- vector("list", nres)
op <- "d>="

i <- 1L
y.data <- sample(c(sample(-10.5:10.5), NA, NaN, Inf, -Inf), 100, TRUE)
basefun <- function(x, y) {
  out <- (x - y) > -prec
  return(out)
}
tests <- test_numeric_y(op, basefun)
enumerate <- enumerate + tests$i # count number of tests
# test results:
expect_equal(
  tests$expected, tests$out
)